## Please note: Alaska and Hawaii are being shifted and are not to scale.
## Getting data from the 2012-2016 5-year ACS
## Parsed with column specification:
## cols(
##   NAME = col_character(),
##   Q22018_GDP = col_double()
## )

Color, Design and Communication

Graphic design standards, especially for color, are an official Good Thing. Branding, visual cueing, consistency. Map colors are hard, especially when dealing with continuous data.

An example of continuous data

The U.S. Bureau of Economic Analysis publishes quarterly statistics on gross domestic product by state.1. For the second quarter 2018, it provided the following.

2018 Second Quarter GDP for the 50 States
NAME GDP
Alabama 221,026,000
Alaska 53,944,000
Arizona 344,636,000
Arkansas 127,951,000
California 2,935,332,000
Colorado 363,845,000
Connecticut 272,745,000
Delaware 74,717,000
Florida 1,029,154,000
Georgia 587,325,000
Hawaii 91,048,000
Idaho 76,187,000
Illinois 859,067,000
Indiana 369,209,000
Iowa 189,299,000
Kansas 164,018,000
Kentucky 209,562,000
Louisiana 249,726,000
Maine 64,203,000
Maryland 414,373,000
Massachusetts 569,290,000
Michigan 532,039,000
Minnesota 362,904,000
Mississippi 113,334,000
Missouri 316,735,000
Montana 49,514,000
Nebraska 123,363,000
Nevada 167,708,000
New Hampshire 85,548,000
New Jersey 626,824,000
New Mexico 98,390,000
New York 1,675,685,000
North Carolina 565,300,000
North Dakota 55,180,000
Ohio 672,055,000
Oklahoma 199,572,000
Oregon 238,687,000
Pennsylvania 788,842,000
Rhode Island 61,168,000
South Carolina 230,213,000
South Dakota 51,721,000
Tennessee 365,129,000
Texas 1,755,634,000
Utah 175,601,000
Vermont 33,522,000
Virginia 532,896,000
Washington 559,011,000
West Virginia 77,748,000
Wisconsin 335,560,000
Wyoming 40,384,000

The two obvious things from the chart are that the numbers differ and that in general the larger states in population are also the larger states in GDP. Whenever making maps of continuous data, they will almost always turn out like this, unless there are marked regional differences and you take the default option.

Here is the GDP data, also with the default option.

Three Problems: One Major, One Important and One Nuisance

The Major Problem: Counts Need Transformation

If we are looking for more interesting differences among states, we should put them on a common footing, so that population size alone doesn’t dominate the effects. For disease, as an example, cases per hundred thousand is commonly used. Here’s what the GDP data looks like on that basis.

The Important Problem

The default continuous blue scale, even with transformed data, still requires squinting to distinguish different values.

Alpha adjustment

We can tinker with that by applying an alpha (density) filter.

The signature for scale_alpha_continuous is

scale_alpha_continuous(…, range = c(0.1, 1))

The range can be narrowed

scale_alpha_continuous(range = c(0.1, 0.75))

or even further lightened

scale_alpha_continuous(range = c(0.1, 0.50))

Beyond Blue Palette Solutions

Viridis palette

Five bright (some might say garish) scales are available.

scale_fill_viridis_c(option="magma")

scale_fill_viridis_c(option="inferno")

scale_fill_viridis_c(option="plasma")

scale_fill_viridis_c(option="viridis")

scale_fill_viridis_c(option="viridis")

Fortunately, the palette can be muted with an alpha density filter

scale_fill_viridis_c(alpha = 0.50, option="cividis")

ggplot’s scale_fill_gradient can also over-ride default colors

The signature of scale_fill_gradient is

scale_fill_gradient(..., low = "#132B43", high = "#56B1F7", space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "fill")

in the blue range. You can adjust that with the arguments to low and high. The following example uses #FBF7F6 and #A97263.

How did I come up with those particular values?

library(munsell)

show_col(seq_gradient_pal("white", mnsl("10R 4/6"))(x))(seq(0, 1, length.out = 25)))

This approach, and other do-it-yourself color palette selection approaches can become a time sink.See R color cheatsheet A better approach is to have a selection of pre-made color cards, and try them out until one of them highlights the differences you are trying to show.

RColorBrewer

This set of palettes plays well with ggplot and should answer most needs.

RColorBrewer

RColorBrewer

The three sections are for sequential (i.e., continuous), divergent (a neutral color in the center progressing toward more intense colors in both direction) or qualitative, for discrete data but coercible into continuous data.

Through ggplot’s scale_colour_distiller function, any of these can be smoothly interpolated into a six-interval scale.

All of the scales can be flipped, working from left-to-right on the palette or from right-to-left. Direction is indicated by direction = -1 or direction = 1.

Color abbreviations:

  • Yl = Yellow
  • Or = Orange
  • Rd = Red
  • Br = Brown
  • Gn = Green
  • Pu = Purple
  • Bu = Blue

There are also:

  • Reds
  • Oranges
  • Greys
  • Greens
  • Blues

The signature for scale_colour_distiller is

scale_fill_distiller(type = “seq”, palette = “YlOrRd”, direction = -1, labels = comma, guide = “colourbar”, aesthetics = “fill”)

Palette Snippets

Usage ggplot_map_object + [name of color scale]



    Alpha_default <- scale_alpha_continuous(range = c(0.1, 1))
  
    Alpha_75      <- scale_alpha_continuous(range = c(0.1, 0.75))
  
    Alpha_50      <- scale_alpha_continuous(range = c(0.1, 0.50))

    magma         <- scale_fill_viridis_c(option="magma")
  
    inferno       <- scale_fill_viridis_c(option="inferno")
  
    plasma        <- scale_fill_viridis_c(option="plasma")
  
    viridis       <- scale_fill_viridis_c(option="viridis")
  
    cividis       <- scale_fill_viridis_c(option="cividis")

    munsell_brown <- scale_fill_gradient(low = "#FBF7F6", high =  "#A97263", space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "fill")

    # RColorBrewer

    BluesNeg1    <- scale_fill_distiller(type = "seq", palette = "Blues", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BluesPos1    <- scale_fill_distiller(type = "seq", palette = "Blues", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuGnNeg1    <- scale_fill_distiller(type = "seq", palette = "BuGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuGnPos1    <- scale_fill_distiller(type = "seq", palette = "BuGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuPuNeg1    <- scale_fill_distiller(type = "seq", palette = "BuPu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BuPuPos1    <- scale_fill_distiller(type = "seq", palette = "BuPu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GnBuNeg1    <- scale_fill_distiller(type = "seq", palette = "GnBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GnBuPos1    <- scale_fill_distiller(type = "seq", palette = "GnBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreensNeg1    <- scale_fill_distiller(type = "seq", palette = "Greens", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreensPos1    <- scale_fill_distiller(type = "seq", palette = "Greens", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreysNeg1    <- scale_fill_distiller(type = "seq", palette = "Greys", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    GreysPos1    <- scale_fill_distiller(type = "seq", palette = "Greys", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrangesNeg1    <- scale_fill_distiller(type = "seq", palette = "Oranges", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrangesPos1    <- scale_fill_distiller(type = "seq", palette = "Oranges", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrRdNeg1    <- scale_fill_distiller(type = "seq", palette = "OrRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    OrRdPos1    <- scale_fill_distiller(type = "seq", palette = "OrRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuGnNeg1    <- scale_fill_distiller(type = "seq", palette = "PuBuGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuGnPos1    <- scale_fill_distiller(type = "seq", palette = "PuBuGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuNeg1    <- scale_fill_distiller(type = "seq", palette = "PuBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuBuPos1    <- scale_fill_distiller(type = "seq", palette = "PuBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuRdNeg1    <- scale_fill_distiller(type = "seq", palette = "PuRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuRdPos1    <- scale_fill_distiller(type = "seq", palette = "PuRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PurplesNeg1    <- scale_fill_distiller(type = "seq", palette = "Purples", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PurplesPos1    <- scale_fill_distiller(type = "seq", palette = "Purples", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdPuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdPu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdPuPos1    <- scale_fill_distiller(type = "seq", palette = "RdPu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RedsNeg1    <- scale_fill_distiller(type = "seq", palette = "Reds", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RedsPos1    <- scale_fill_distiller(type = "seq", palette = "Reds", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnBuNeg1    <- scale_fill_distiller(type = "seq", palette = "YlGnBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnBuPos1    <- scale_fill_distiller(type = "seq", palette = "YlGnBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnNeg1    <- scale_fill_distiller(type = "seq", palette = "YlGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlGnPos1    <- scale_fill_distiller(type = "seq", palette = "YlGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrBrNeg1    <- scale_fill_distiller(type = "seq", palette = "YlOrBr", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrBrPos1    <- scale_fill_distiller(type = "seq", palette = "YlOrBr", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrRdNeg1    <- scale_fill_distiller(type = "seq", palette = "YlOrRd", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    YlOrRdPos1    <- scale_fill_distiller(type = "seq", palette = "YlOrRd", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BrBGNeg1    <- scale_fill_distiller(type = "seq", palette = "BrBG", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    BrBGPos1    <- scale_fill_distiller(type = "seq", palette = "BrBG", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PiYGNeg1    <- scale_fill_distiller(type = "seq", palette = "PiYG", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PiYGPos1    <- scale_fill_distiller(type = "seq", palette = "PiYG", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PRGnNeg1    <- scale_fill_distiller(type = "seq", palette = "PRGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PRGnPos1    <- scale_fill_distiller(type = "seq", palette = "PRGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuOrNeg1    <- scale_fill_distiller(type = "seq", palette = "PuOr", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PuOrPos1    <- scale_fill_distiller(type = "seq", palette = "PuOr", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdBuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdBuPos1    <- scale_fill_distiller(type = "seq", palette = "RdBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdGyNeg1    <- scale_fill_distiller(type = "seq", palette = "RdGy", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdGyPos1    <- scale_fill_distiller(type = "seq", palette = "RdGy", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlBuNeg1    <- scale_fill_distiller(type = "seq", palette = "RdYlBu", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlBuPos1    <- scale_fill_distiller(type = "seq", palette = "RdYlBu", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlGnNeg1    <- scale_fill_distiller(type = "seq", palette = "RdYlGn", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    RdYlGnPos1    <- scale_fill_distiller(type = "seq", palette = "RdYlGn", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    SpectralNeg1    <- scale_fill_distiller(type = "seq", palette = "Spectral", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    SpectralPos1    <- scale_fill_distiller(type = "seq", palette = "Spectral", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    AccentNeg1    <- scale_fill_distiller(type = "seq", palette = "Accent", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    AccentPos1    <- scale_fill_distiller(type = "seq", palette = "Accent", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Dark2Neg1    <- scale_fill_distiller(type = "seq", palette = "Dark2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Dark2Pos1    <- scale_fill_distiller(type = "seq", palette = "Dark2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PairedNeg1    <- scale_fill_distiller(type = "seq", palette = "Paired", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    PairedPos1    <- scale_fill_distiller(type = "seq", palette = "Paired", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel1Neg1    <- scale_fill_distiller(type = "seq", palette = "Pastel1", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel1Pos1    <- scale_fill_distiller(type = "seq", palette = "Pastel1", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel2Neg1    <- scale_fill_distiller(type = "seq", palette = "Pastel2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Pastel2Pos1    <- scale_fill_distiller(type = "seq", palette = "Pastel2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set1Neg1    <- scale_fill_distiller(type = "seq", palette = "Set1", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set1Pos1    <- scale_fill_distiller(type = "seq", palette = "Set1", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set2Neg1    <- scale_fill_distiller(type = "seq", palette = "Set2", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set2Pos1    <- scale_fill_distiller(type = "seq", palette = "Set2", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set3Neg1    <- scale_fill_distiller(type = "seq", palette = "Set3", direction = -1, labels = comma,  guide = "colourbar", aesthetics = "fill")

    Set3Pos1    <- scale_fill_distiller(type = "seq", palette = "Set3", direction = 1, labels = comma,  guide = "colourbar", aesthetics = "fill")

Credits

  1. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
  2. Simon Urbanek and Jeffrey Horner (2015). Cairo: R graphics device using cairo graphics library for creating high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG, PostScript) and display (X11 and Win32) output. R package version 1.5-9. https://CRAN.R-project.org/package=Cairo
  3. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016
  4. Hao Zhu (2018). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 0.9.0. https://CRAN.R-project.org/package=kableExtra
  5. Yihui Xie (2018). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.20. Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963 Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria 6. Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595
  6. Charlotte Wickham (2018). munsell: Utilities for Using Munsell Colours. R package version 0.5.0. https://CRAN.R-project.org/package=munsell
  7. Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer
  8. Hadley Wickham (2018). scales: Scale Functions for Visualization. R package version 1.0.0.https://CRAN.R-project.org/package=scales
  9. Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal, https://journal.r-project.org/archive/2018/RJ-2018-009/
  10. Kyle Walker (2018). tidycensus: Load US Census Boundary and Attribute Data as ‘tidyverse’ and22 ‘sf’-Ready Data Frames. R package version 0.8.1. https://CRAN.R-project.org/package=tidycensus
  11. Hadley Wickham (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse

  1. “Source: Bureau of Economic Analysis, Gross Domestic Product By State, 2nd Quarter 2018 https://goo.gl/Jc6XyK”↩